Determination of neuropsychiatric therapy mechanisms of action

ABSTRACT

A computer implemented method, apparatus, and computer program product of determining mechanisms of action for therapies. A first set of brain scans for each subject in a plurality of subjects generated at a first time period and a second set of brain scans for each subject generated at a second time period are received. Each subject is diagnosed with a given condition and received a given therapy. A set of changes in the set of brain scans is identified for the each subject based on a comparison of a first set of regions of interest in the first set of scans for each subject with a second set of regions of interest in the second set of scans for each subject. A set of typical changes attributable to the given therapy is identified. A mechanism of action for the given therapy is generated based on the set of typical changes.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is related generally to a data processing systemand in particular to a method and apparatus for determining a mechanismof action of therapy. More particularly, the present invention isdirected to a computer implemented method, apparatus, and computerusable program code for automatically determining the mechanism ofaction for neuropsychiatric therapies via automation of the assessmentof neuroimage data and medical literature.

2. Description of the Related Art

Neuropsychiatric conditions typically have neurological featuresassociated with disorders of the nervous system, as well as psychiatricfeatures. Neuropsychiatric conditions may be treated using a variety oftherapies, such as talk therapy, behavioral therapy, chemical therapy,and/or mechanical therapy. Chemical therapy refers to pharmacotherapy,such as, the utilization of drugs. Mechanical therapy includeselectroconvulsive therapies (ECT) and deep brain stimulation (DBS).These therapies may be used separately or may be used in combination totreat patients. However, some patients may not receive the mosteffective treatments available due to difficulties in accuratelydiagnosing patients with neuropsychiatric conditions and determiningaccurate mechanisms of action in drug therapies. The determination ofmechanisms of action in drugs may be a time consuming, tedious, andexpensive process, particularly in complex cases.

BRIEF SUMMARY OF THE INVENTION

According to one embodiment of the present invention, a computerimplemented method, apparatus, and computer program product fordetermining mechanisms of action for therapies is provided. The processreceives a plurality of brain scans for a plurality of subjects. Theplurality of brains scans comprises a first set of brain scans for eachsubject in the plurality of subjects generated at a first time periodand a second set of brain scans for each subject in the plurality ofsubjects generated at a second time period. Each subject in theplurality of subjects is diagnosed with a given condition and eachsubject is receiving a given therapy. The given therapy may be apharmacotherapy, a mechanical therapy, or talk therapy.

A set of electronic medical literature sources is automatically searchedfor portions of medical literature describing the given therapy. A firstset of regions of interest in the first set of brain scans for eachsubject and a second set of regions of interest in the second set ofbrain scans are automatically identified for each subject. A set ofchanges in the set of brain scans is identified for each subject basedon a comparison of the first set of regions of interest for each subjectwith the second set of regions of interest for each subject. The set ofchanges comprises indicators of change occurring after each subjectbegins receiving the given therapy. The set of changes for each subjectis analyzed with the portions of the medical literature describing thegiven therapy to identify a set of typical changes attributable to thegiven therapy. A mechanism of action for the given therapy is generatedbased on the set of typical changes.

In another embodiment, the plurality of subjects comprises subjects fromvarious demographic groups. Each subject in the plurality of subjectsreceives the given therapy after the first set of scans were taken atthe first time and before the second set of scans were taken at thesecond time. Additional subject data for each subject may alsooptionally be analyzed with the set of typical changes and portions ofthe medical literature associated with the given therapy to generate themechanism of action. The additional subject data may include clinicaldata, subject medical history, subject data, and/or cognitive data.

In one embodiment, the portions of the medical literature may includemedical literature relevant to the given condition, the given therapy,and the set of changes occurring over time to form the portions of themedical literature. The portions of the medical literature are analyzedwith the set of typical changes occurring over time to derive themechanism of action for the given therapy. The mechanism of actioncomprises a set of links to the portions of the medical literatureassociated with the given therapy and each change in the set of typicalchanges.

In yet another embodiment, a set of brain scans for a set of healthysubjects in various demographic groups is received to form the baselinenormal scans. The baseline normal scans are analyzed to identify anormal appearance of areas in normal brain scans, wherein a normal brainscan is a scan that does not show indications of disease orabnormalities in the areas in the normal brain scans. The set of changesfor each subject is compared with the baseline normal scans to identifythe drug mechanism of action.

In another embodiment, other therapies being applied to a given subjectin the plurality of subjects and/or other medical conditions in theplurality of subjects are identified. Changes in the set of scans forthe given subject that are attributable to the other therapies and/orthe other medical conditions are identified as uncorrelated changes. Theuncorrelated changes are removed from the set of typical changes.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a pictorial representation of a network of data processingsystems in which illustrative embodiments may be implemented;

FIG. 2 is a block diagram of a data processing system in whichillustrative embodiments may be implemented;

FIGS. 3A and 3B is a block diagram of a neuroimage mapping manager and atherapy mechanism generator in accordance with an illustrativeembodiment;

FIG. 4 is a block diagram of a magnetic resonance imaging brain scanhaving regions of interest in accordance with an illustrativeembodiment;

FIG. 5 is a positron emissions tomography brain scan having regions ofinterest in accordance with an illustrative embodiment;

FIG. 6 is a block diagram of digital video analysis for generatingbehavioral data in accordance with an illustrative embodiment;

FIG. 7 is a flowchart of a process for generating baseline control scansin accordance with an illustrative embodiment;

FIG. 8 is a flowchart of a process for identifying regions of interestcorrelated with relevant portions of the medical interest in accordancewith an illustrative embodiment;

FIG. 9 is a flowchart of a process for identifying changes that areattributable to a given therapy in accordance with an illustrativeembodiment; and

FIG. 10 is a flowchart of a process for generating a mechanism of actionfor a given therapy in accordance with an illustrative embodiment.

DETAILED DESCRIPTION OF THE INVENTION

As will be appreciated by one skilled in the art, the present inventionmay be embodied as a system, method or computer program product.Accordingly, the present invention may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,the present invention may take the form of a computer program productembodied in any tangible medium of expression having computer usableprogram code embodied in the medium.

Any combination of one or more computer usable or computer readablemedium(s) may be utilized. The computer-usable or computer-readablemedium may be, for example but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,or device. More specific examples (a non-exhaustive list) of thecomputer-readable medium would include the following: an electricalconnection having one or more wires, a portable computer diskette, ahard disk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), anoptical fiber, a portable compact disc read-only memory (CDROM), anoptical storage device, or a magnetic storage device. Note that thecomputer-usable or computer-readable medium could even be paper oranother suitable medium upon which the program is printed, as theprogram can be electronically captured, via, for instance, opticalscanning of the paper or other medium, then compiled, interpreted, orotherwise processed in a suitable manner, if necessary, and then storedin a computer memory. In the context of this document, a computer-usableor computer-readable medium may be any medium that can contain, store,or transport the program for use by or in connection with theinstruction execution system, apparatus, or device.

Computer program code for carrying out operations of the presentinvention may be written in any combination of one or more programminglanguages, including an object oriented programming language such asJava, Smalltalk, C++ or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN) or a wide area network(WAN), or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider).

The present invention is described below with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments of the invention. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerprogram instructions.

These computer program instructions may be provided to a processor of ageneral purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions, which execute via the processor of the computer orother programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer program instructions may also bestored in a computer-readable medium that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide processes for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

FIG. 1 depicts a pictorial representation of a network of dataprocessing systems in which illustrative embodiments may be implemented.Network data processing system 100 is a network of computers in whichthe illustrative embodiments may be implemented. Network data processingsystem 100 contains network 102, which is the medium used to providecommunications links between various devices and computers connectedtogether within network data processing system 100. Network 102 mayinclude connections, such as wire, wireless communication links, orfiber optic cables.

In the depicted example, server 104 and server 106 connect to network102 along with storage unit 108. In addition, clients 110, 112, and 114connect to network 102. Clients 110, 112, and 114 may be, for example,personal computers or network computers. In the depicted example, server104 provides data, such as boot files, operating system images, andapplications to clients 110, 112, and 114. Clients 110, 112, and 114 areclients to server 104 in this example. Network data processing system100 may include additional servers, clients, and other devices notshown.

In the depicted example, network data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, network data processing system 100 also may be implemented as anumber of different types of networks, such as for example, an intranet,a local area network (LAN), or a wide area network (WAN). FIG. 1 isintended as an example, and not as an architectural limitation for thedifferent illustrative embodiments.

With reference now to FIG. 2, a block diagram of a data processingsystem is shown in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such as server104 or client 110 in FIG. 1, in which computer usable program code orinstructions implementing the processes may be located for theillustrative embodiments. In this illustrative example, data processingsystem 200 includes communications fabric 202, which providescommunications between processor unit 204, memory 206, persistentstorage 208, communications unit 210, input/output (I/O) unit 212, anddisplay 214.

Processor unit 204 serves to execute instructions for software that maybe loaded into memory 206. Processor unit 204 may be a set of one ormore processors or may be a multi-processor core, depending on theparticular implementation. Further, processor unit 204 may beimplemented using one or more heterogeneous processor systems in which amain processor is present with secondary processors on a single chip. Asanother illustrative example, processor unit 204 may be a symmetricmulti-processor system containing multiple processors of the same type.

Memory 206 and persistent storage 208 are examples of storage devices. Astorage device is any piece of hardware that is capable of storinginformation either on a temporary basis and/or a permanent basis. Memory206, in these examples, may be, for example, a random access memory orany other suitable volatile or non-volatile storage device. Persistentstorage 208 may take various forms depending on the particularimplementation. For example, persistent storage 208 may contain one ormore components or devices. For example, persistent storage 208 may be ahard drive, a flash memory, a rewritable optical disk, a rewritablemagnetic tape, or some combination of the above. The media used bypersistent storage 208 also may be removable. For example, a removablehard drive may be used for persistent storage 208.

Communications unit 210, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 210 is a network interface card. Communications unit210 may provide communications through the use of either or bothphysical and wireless communications links.

Input/output unit 212 allows for input and output of data with otherdevices that may be connected to data processing system 200. Forexample, input/output unit 212 may provide a connection for user inputthrough a keyboard and mouse. Further, input/output unit 212 may sendoutput to a printer. Display 214 provides a mechanism to displayinformation to a user.

Instructions for the operating system and applications or programs arelocated on persistent storage 208. These instructions may be loaded intomemory 206 for execution by processor unit 204. The processes of thedifferent embodiments may be performed by processor unit 204 usingcomputer implemented instructions, which may be located in a memory,such as memory 206. These instructions are referred to as program code,computer usable program code, or computer readable program code that maybe read and executed by a processor in processor unit 204. The programcode in the different embodiments may be embodied on different physicalor tangible computer readable media, such as memory 206 or persistentstorage 208.

Program code 216 is located in a functional form on computer readablemedia 218 that is selectively removable and may be loaded onto ortransferred to data processing system 200 for execution by processorunit 204. Program code 216 and computer readable media 218 form computerprogram product 220 in these examples. In one example, computer readablemedia 218 may be in a tangible form, such as, for example, an optical ormagnetic disc that is inserted or placed into a drive or other devicethat is part of persistent storage 208 for transfer onto a storagedevice, such as a hard drive that is part of persistent storage 208. Ina tangible form, computer readable media 218 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory that is connected to data processing system 200. The tangibleform of computer readable media 218 is also referred to as computerrecordable storage media. In some instances, computer recordable media218 may not be removable.

Alternatively, program code 216 may be transferred to data processingsystem 200 from computer readable media 218 through a communicationslink to communications unit 210 and/or through a connection toinput/output unit 212. The communications link and/or the connection maybe physical or wireless in the illustrative examples. The computerreadable media also may take the form of non-tangible media, such ascommunication links or wireless transmissions containing the programcode.

The different components illustrated for data processing system 200 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different illustrativeembodiments may be implemented in a data processing system includingcomponents in addition to or in place of those illustrated for dataprocessing system 200. Other components shown in FIG. 2 can be variedfrom the illustrative examples shown.

As one example, a storage device in data processing system 200 is anyhardware apparatus that may store data. Memory 206, persistent storage208, and computer readable media 218 are examples of storage devices ina tangible form.

In another example, a bus system may be used to implement communicationsfabric 202 and may be comprised of one or more buses, such as a systembus or an input/output bus. Of course, the bus system may be implementedusing any suitable type of architecture that provides for a transfer ofdata between different components or devices attached to the bus system.Additionally, a communications unit may include one or more devices usedto transmit and receive data, such as a modem or a network adapter.Further, a memory may be, for example, memory 206 or a cache such asfound in an interface and memory controller hub that may be present incommunications fabric 202.

The illustrative embodiments recognize that patients may suffer from thenegative side effects of effective therapies and/or trails ofineffective therapies due to a lack of detailed information describingmechanisms of action. Furthermore, the mechanism of action for a giventherapy may be altered or modified due to drug interactions andutilization of other therapies in conjunction with the given therapy. Insuch cases, it may be difficult or impossible to clearly delineate amechanism of action for the multiple therapies utilized in these complexcases.

Therefore, one embodiment provides a computer implemented method,apparatus, and computer program product for determining mechanisms ofaction for therapies. The process receives a plurality of brain scansfor a plurality of subjects. The subjects may be patients, volunteers,participants in a drug study, paid participants, or any other humansubjects. The plurality of brains scans comprises a first set of brainscans for each subject in the plurality of subjects generated at a firsttime period and a second set of brain scans for each subject in theplurality of subjects generated at a second time period. As used herein,the term “set” refers to one or more, unless indicated otherwise. Thus,the first set of brain scans is a set of one or more brain scans. Abrain scan may be a positron emission tomography (PET) scan, a magneticresonance imaging (MRI) scan, an x-ray scan, or any other type of brainscan.

Each subject in the plurality of subjects is diagnosed with a givencondition and each subject is receiving a given therapy. The giventherapy may be a pharmacotherapy, a mechanical therapy, or talk therapy.The given condition in this embodiment is a neuropsychiatric condition.

A set of electronic medical literature sources is automatically searchedfor portions of medical literature describing the given therapy. A firstset of regions of interest in the first set of brain scans for eachsubject and a second set of regions of interest in the second set ofbrain scans are automatically identified for each subject. A region ofinterest is an area in a brain scan that includes a set of indicatorsassociated with the mechanism of action of the given therapy. Anindicator is a structural feature, level of brain chemistry, indicatorof brain metabolism, indicator of active regions and/or inactive regionsof the brain, and other features shown in a brain scan.

A set of changes in the set of brain scans is identified for eachsubject based on a comparison of the first set of regions of interestfor each subject with the second set of regions of interest for eachsubject. The set of changes includes indicators of change occurringafter each subject begins receiving the given therapy. An indicator isany feature or characteristic of an area in a scan. The set of changesfor each subject is analyzed with the portions of the medical literaturedescribing the given therapy to identify a set of typical changesattributable to the given therapy. A mechanism of action for the giventherapy is generated based on the set of typical changes.

FIGS. 3A and 3B is a block diagram of a neuroimage mapping manager inaccordance with an illustrative embodiment. Neuroimage mapping manager300 is software for analyzing subject brain scans to identify regions ofinterest in the brain scans and generate links to portions of interestin the medical literature. Computer 301 may be implemented in any typeof computing device, such as, without limitation, a server, a client, alaptop computer, a personal digital assistant (PDA), a smart phone, orany other known or available computing device shown in FIG. 1 and FIG.2.

Neuroimage analyzer 302 receives first set of scans 303. First set ofscans 303 is a set of one or more scans of a subject's brain generatedat a first time. The first time may be a time prior to beginningimplementation of one or more therapies or a given time after beginningone or more therapies. Second set of scans 304 is a set of scans of thesubject generated at a second time. The second time is a given period oftime after the first time. The second time may be, for example andwithout limitation, a day, a week, a month, six months, a year, twoyears, or any other time period after the first time. The second time isa given amount of time after beginning implementation of one or moretherapies in a treatment plan to treat a subject.

First set of scans 303 and second set of scans 304 may includefunctional magnetic resonance imaging (FMRI) scans, structural magneticresonance imaging (sMRI) scans, positron emission tomography (PET)scans, and/or any other type of brain scans. In other words, first setof scans 303 may include only positron emission tomography scans, onlymagnetic resonance imaging scans, or a combination of positron emissiontomography scans and magnetic resonance imaging scans. The scans infirst set of scans 303 may be generated by one or more scanning devices,such as scanning device 305.

Scanning device 305 may be implemented as one or more of a functionalmagnetic resonance imaging device, a structural magnetic resonanceimaging device, a positron emission tomography device, or any other typeof device for generating scans of a human subject's brain. As usedherein, the term “subject” is not limited to a patient admitted in ahospital. The term “subject” may refer to any person obtaining medicalcare, consulting a medical practitioner, participating in a medicalstudy, obtaining medical advice, or otherwise participating in medicaltests and/or medical procedures.

Scanning device 305 in this example is a single scanning device.However, scanning device 305 may also include two or more scanningdevices. Scanning device 305 optionally saves the scans of the subject'sbrain in data storage device 306. Data storage device 306 may beimplemented as a hard drive, a flash memory, a main memory, read onlymemory (ROM), a random access memory (RAM), or any other type of datastorage device. Data storage may be implemented in a single data storagedevice or a plurality of data storage devices. Thus, neuroimage analyzer302 may receive the scans in first set of scans 304 from scanning device305 as each scan is generated, or neuroimage analyzer 302 may retrievethe scans from a pre-generated set of scans stored in data storagedevice 306.

Comparator 307 is a software component that compares first set of scans304 to baseline normal scans 308 and/or baseline abnormal scans 310 toidentify regions of interest 312 first set of scans 303 and regions ofinterest 313 in second set of scans. A region of interest is an area ina scan that shows an indication of a potential abnormality, a potentialillness, a potential disease, a potential condition, or any otherdeviation from what would be expected in a scan of the region for ahealthy individual having similar characteristics as the subject. Thesimilar characteristics may include, without limitation, an age range ofthe subject, gender, pre-existing conditions, or other factorsinfluencing the development, function, structure, and appearance of anarea of the brain as shown in a scan.

Baseline normal scans 308 may include, without limitation, a set of oneor more brain scans for average, healthy subjects having one or morecharacteristics in common with the subject. The characteristics incommon may be age, gender, pre-existing conditions, profession, place ofresidence, nationality, or any other characteristic. For example, if thesubject is a sixteen year old female, baseline normal scans 308 mayinclude scans of normal, healthy female subjects between the ages offourteen and eighteen. Comparator 307 compares one or more areas in eachscan in first set of scans 303 with corresponding areas in one or morescans in baseline normal scans 308 to identify areas of the subject'sscans that are consistent with the scans of normal, healthy subjects andto identify areas of the scans that are inconsistent with the scans ofnormal, healthy subjects. An area in a scan that is inconsistent withthe corresponding areas in baseline normal scans 308 are identified as aregion of interest in regions of interest 312. A region identified inregions of interest 312 and 313 may indicate a potential abnormality,illness, or condition. However, each region in regions of interest 312and/or 313 are not required to definitively indicate an abnormality,illness, condition, or other deviation from the norm.

Baseline abnormal scans 310 is a set of one or more scans of subjectshaving one or more characteristics in common with the subject anddiagnosed with an identified condition. The identified condition may bea disease, an illness, a deformity, an abnormality, or any otheridentified deviation from the norm. For example, if the subject is amale, age thirty five, and diagnosed with diabetes, the baselineabnormal scans may include scans of male subjects between the ages ofthirty and forty and having a variety of known neuropsychiatricdisorders. Comparator 307 compares regions in each scan in first set ofscans 303 with one or more scans in baseline abnormal scans 310 toidentify regions of interest in the subject's scans that showindications of disorders, illness, disease, or abnormalities. A regionin a scan may show indications of a potential illness, condition,abnormality, or neuropsychiatric disorder if the region in the subject'sscan is consistent with a corresponding region in a brain scan of asubject having a known illness, condition, abnormality, orneuropsychiatric disorder. Thus, neuroimage analyzer 302 analyzes firstset of scans 303 to identify regions of interest in the scans based onbaseline normal scans and/or baseline disorder scans for identifiedillnesses, abnormalities, diseases, disorders, or other knownconditions.

Medical data and text analytics 314 is a software component forsearching set of electronic medical literature sources 316 for medicalliterature relevant to regions of interest 312 in first set of scans304. Set of electronic medical literature sources 316 is a set of one ormore sources of medical literature 318. Set of electronic medicalliterature sources 316 may include both online medical literaturesources that are accessed by medical data and text analytics 314 via anetwork connection, as well as off-line medical literature sources thatmay be accessed without a network connection. An example of anelectronic medical literature source includes, without limitation,PUBMED. Medical literature 318 is any literature, journal article,medical study results, medical text, pharmaceutical studies, or anyother medical information in an electronic format. Medical literature318 may include scans 320, such as magnetic resonance imaging scans,positron emission tomography scans, or any other type of brain scans.

Medical data and text analytics 314 comprises search engine 322. Searchengine 322 is any type of known or available information retrievalsoftware for locating medical literature that is relevant to regions ofinterest 312 in set of electronic medical literature sources 316. Searchengine 322 may be software for searching data storage devices on acomputer system or a web search tool for searching for medicalinformation on the World Wide Web. Search engine 322 may also makequeries into databases, information systems, and other medicalliterature information sources to locate information relevant to regionsof interest 312.

Data mining 324 is a software tool for searching through informationavailable from one or more sources and retrieving medical informationrelevant to regions of interest 312. Data mining 324, search engine 322,or any other software for locating relevant information may be used tosearch set of electronic medical literature sources 316 for relevantmedical literature. Searching through the information from one or moresources may include, without limitation, using at least one of datamining, search engines, pattern recognition, queries to identify therelevant medical literature in the medical literature available from theset of electronic medical literature sources, data mining cohort,pattern recognition cohort, search engine cohort, or any other cohortappliance of interest. The term “at least one” refers to one or more andin any combination. Thus, the searching may include data mining only,data mining and pattern recognition, search engines, patternrecognition, and queries, or any other combination.

A cohort is a group of one or more objects having a commoncharacteristic. For example, a data mining cohort may be, withoutlimitation, a group of one or more objects associated with performingdata mining techniques to identify desired data from a data source. Apattern recognition cohort may be, without limitation, a group ofpattern recognition software applications that identify patterns indata, such as medical data.

Parser 326 is software for parsing medical literature 318 text into aform suitable for further analysis and processing. Parser 326 may beimplemented as any type of known or available parser. Correlation engine328 correlates portions of medical literature 318 with regions ofinterest 312 to form portions of medical literature 318 that arerelevant or associated with regions of interest 312. A portion ofmedical literature is a section of medical literature text and/or one ormore scans that describes a region of interest, describes a condition,illness, deformity, abnormality, disease, or other cause for anappearance of a region of interest, an area in a scan that is the sameor similar to an area of interest, an area in a scan in scans 320 or aportion of text in a medical literature document that is otherwiseassociated with a characteristic, feature, structure, indicator of brainchemistry, indicator of brain function, or other feature shown in anarea of interest in a subject's brain scan.

For example, if a region of interest in subject's brain scan indicatesan enlargement of a brain ventricle, a scan in scans 320 in medicalliterature 318 showing a similar enlargement of the brain ventricle is aportion of medical literature that is relevant or associated withregions of interest 312. Likewise, if a section of a medical journalarticle in an electronic format in medical literature 318 describesvarious causes of enlargement of a brain ventricle, that section of themedical literature is also relevant or associated with regions ofinterest 312. Thus, in this example, portions of medical literature 318include both the scan showing the enlargement of the ventricle in adifferent subject and the portion of the medical journal articlediscussing possible causes of an enlargement of the ventricles insubjects.

In this manner, medical data and text analytics 314 is capable ofautomatically searching for electronic medical literature, identifyingportions of the medical literature that are relevant to a particularsubject's diagnosis and/or treatment, and correlate each item, such as ascan or a section in a journal article, to each region of interest inthe subject's brain scans. When a user wishes to view all the relevantmedical literature associated with a particular region of interest, theuser can simply request all the portions of medical literaturecorrelated to the particular region of interest. In response, neuroimagemapping manager 300 only provides the portions of medical literature 318from a plurality of medical literature sources that may be useful to theuser, rather than providing the full text of all medical journalarticles that have certain key words or search phrases, as is currentlydone.

Neuroimage mapping manager 300 may also generate a set of links toportions of medical literature 318 describing or associated with regionsof interest 312 and/or regions of interest 313. Regions of interest 312and/or 313 may also optionally include an identification of a sourceand/or citation for the source of each portion of medical literaturelinked to the regions of interest. The set of links to portions ofmedical literature 318 may be embedded in first set of scans 303 and/orsecond set of scans 304, or embedded within regions of interest 312 infirst set of scans 303 and/or regions of interest 313 in second set ofscans 304. The set of links to portions of medical literature 318 mayalso optionally be presented as a separate result apart from first setof scans 304 and/or apart from regions of interest 312. In anotherembodiment, the set of links to portions of medical literature 318 areembedded in an electronic medical file for the subject or a file forbrain scan results for one or more subjects. A user selects a link inthe set of links to view a portion of medical literature associated witha region of interest. In such a case, the portions of medical literature318 in the subject's medical file may include a set of links to firstset of scans 303 and second set of scans 304 and/or a set of links toregions of interest 312 and 313. In such a case, each portion of themedical literature, such as a scan or a section of a medical journalarticle, may include a link to the region of interest that is associatedwith or relevant to that portion of the medical literature. Likewise,all the portions of the medical literature that are relevant to aparticular region of interest may include a single link to thatparticular region of interest rather than each portion of the medicalliterature including a separate link to the particular region ofinterest or regions of interest associated with the portions of themedical literature.

The portion of medical literature may be a scan only, text only, or acombination of text and one or more scans. The portion of medicalliterature may be an entire or complete item, such as a complete medicaljournal article or a complete section of a medical textbook, if theentire journal article or complete section of the medical text isrelevant to the features shown in a particular region of interest. Theportion of medical literature may also be a portion of a journalarticle, a portion of a section of a medical textbook, or other part ofan item of medical literature. In such a case, a user may optionallyselect to view the entire journal article or the entire medical textrather than viewing only the relevant portion of the journal article ormedical text.

In this embodiment, baseline normal scans 308 and baseline abnormalscans 310 are pre-generated and available for retrieval from datastorage device 306. However, in another embodiment, medical data andtext analytics 314 searches set of electronic medical literature sources316 for scans of normal, healthy subjects to create baseline normalscans 308. Medical data and text analytics 316 also searches set ofelectronic medical literature sources 316 for scans of subjects havingknown abnormalities, deformities, illnesses, ailments, diseases, orother neuropsychiatric disorders to create baseline abnormal scans 310.

Thus, neuroimage mapping manager 300 provides data and text analytics toautomatically determine regions of a subject's brain affected byneuropsychiatric conditions and/or other illness or abnormality asdepicted in functional neuroimage data. Neuroimage data is dataassociated with a brain scan, such as functional magnetic resonanceimaging and positron emission tomography scans. Neuroimage mappingmanager 300 applies technologies to data, such as heuristics, whichautomatically correlate the features identified in regions of interest312 with relevant portions of medical literature 318 that describesregions of interest 312.

Comparator 307 also compares regions of interest 312 in first set ofscans 304 with regions of interest 314 in second set of scans 305 toidentify one or more changes in the regions of interest 332 over time.Changes in regions of interest 332 is an identification of changes ordifferences in the regions of interest in first set of scans 303 andsecond set of scans 304. For example, if a comparison of first set ofscans 303 with second set of scans 304 shows that brain metabolism in afirst region of interest has increased and a disruption of activity hasoccurred in a second region, these changes are identified in changes inregions of interest 332. Change in regions of interest 332 may alsoinclude a set of links to portions of medical literature 318 associatedwith or describing the changes.

Input/output 334 may be implemented as any type of input and/or outputdevice for presenting output to a user and receiving input from a user.For example, input/output 334 may present regions of interest 312 to auser and/or receive a selection of one or more regions of interest froma user. Input/output 334 may also be used to present set of diagnoses,treatment plans, or other information to a user. Neuroimage analyzer 302may optionally present the automatically selected regions of interest tothe user using input/output 334. The automatically selected regions ofinterest may be presented using a display device to present the regionsof interest in a visual format, using an audio device to present theregions of interest to the user in an audio format, using a tactileinterface that may be read by the visually impaired, using a combinationof audio and visual devices, using a combination of audio and tactiledevices, or any other presentation device.

The user may utilize input/output 334 to choose to select one or moreadditional regions of interest in first set of scans 303 and/or secondset of scans 304. In such a case, neuroimage analyzer 302 adds themanually selected set of one or more regions of interest to regions ofinterest 312. In one embodiment, the regions of interest that are notautomatically selected by neuroimage analyzer 302 and/or regions ofinterest that are not manually selected by the user are automaticallyremoved by neuroimage analyzer 302. In another embodiment, the user maychoose to manually de-select or remove one or more regions of interestthat was automatically selected by neuroimage analyzer 302. In such acase, neuroimage analyzer 302 automatically removes the one or moreregions of interest selected for removal by the user from regions ofinterest 312.

In another embodiment, neuroimage mapping manager 300 makes adetermination as to whether indicators correlate with the subject'sclinical data. Clinical data 336 is data describing the results ofclinical laboratory tests. Clinical data 336 may include, withoutlimitation, urinalysis tests, blood tests, thyroid tests, biopsyresults, cultures, electrolyte tests, genetic tests, bone marrow tests,tests for the presence of viral agents/illness, tests for the presenceof bacterial agents/illnesses, hormone tests, or any other type oflaboratory tests. Clinical data 336 describes the presence of substancesin the blood, urine, tissue, hormone levels, body chemistry, and bodyfluids. Clinical data 336 may be relevant to diagnosis or therapy for aparticular condition.

Moreover, clinical data 336 may reveal causes of one or more features inthe brain scans. For example, clinical tests may indicate mercurypoisoning or other substances in the blood that may be responsible forthe abnormal appearance of a region in a brain scan. Clinical data 336for a particular subject may be available on data storage device 306,obtained from a remote data storage device via a network connection,and/or may be manually input to neuroimage mapping manager throughinput/output device 334. If the features in a region of interestcorrelate with clinical data 336, neuroimage mapping manager 300identifies the correlations. The correlations may be provided as linksto information embedded within regions of interest 312 and/or 313 orprovided separately from the regions of interest.

Subject medical history 338 is a record of the subject's past andcurrent medical treatments, prescribed drugs, medical procedures,diagnoses, treating physicians, known allergies, and/or any othermedical information associated with the subject. Neuroimage mappingmanager 300 may correlate information in subject medical history thatmay be responsible for an appearance or presence of a feature in aregion of interest with that particular region in regions of interest312.

For example, if subject medical history 338 indicates that the subjectsuffered a head trauma in a car accident when the subject was a childthat led to structural damage in a particular area of the brain, thatinformation is linked to the region of interest corresponding to thearea of the brain where the head trauma occurred. Likewise, if thesubject had brain surgery to prevent or lessen the effects of seizuresand the epilepsy surgery effects brain function in one or more areas ofthe brain, the regions of interest that are correlated to the areas ofthe brain effected by the epilepsy surgery are identified in regions ofinterest 312 with a link to the portion of the subject medical history338 discussing the epilepsy surgery and effects of the epilepsy surgery.

In another embodiment, neuroimage mapping manager 300 makes adetermination as to whether change in regions of interest 332 correlatewith the subject's clinical data or medical history. For example,clinical tests may indicate mercury poisoning or other substances in theblood that may be responsible for the changes in brain chemistry and/orbrain function shown in the brain scans. If the changes in regions ofinterest 332 correlate with the clinical data or medical history,neuroimage mapping manager 300 identifies the correlations in change inregions of interest 332 or the correlations may be identified in aseparate output provided separately from change in regions of interestover time 332.

Behavioral data 340 is metadata describing the appearance of thesubject, the actions of the subject, events associated with the subject,and any other behavior related data. For example, and withoutlimitation, behavioral data 340 may indicate that the subject is wearingmultiple layers of clothing on a warm summer day. The behavioral datamay describe behavioral tics, such as verbal tics, unprovoked use ofprofanity, locking and unlocking doors, turning lights on and off,lacing and unlacing shoes, or other repetitive behaviors.

Behavioral data 340 may also describe behaviors, such as, withoutlimitation, pacing, a running monologue or talking with oneself, anappearance of confusion, or other actions. The behavioral data may alsodescribe an appearance of a person's face and emotions apparent on thesubject's face. For example, and without limitation, the behavioral datamay describe an angry look, such as frowning and dilated pupils inconjunction with utilization of a loud voice and throwing objects toidentify angry or hostile behavior. Behavioral data 340 may indicatethat the subject sat in a single location, did not speak, did not reactto other people or external stimuli, and had a fixed stare for a givenperiod of time to indicate that the subject is unemotional, dissociated,or catatonic. Behavioral data 340 may be provided manually by a user orgenerated automatically by digital video analysis software, such as,without limitation, International Business Machines (IBM) smartsurveillance system (S3).

Cognitive data 342 is data describing results of cognitive tests andpsychological evaluations. Cognitive data 342 may include results ofRorschach ink blot tests, memory tests, intelligence quotient (IQ)tests, problem solving, language skills tests, perception tests, andother results of cognitive and psychological evaluations. Cognitive data342 may be entered by a user manually using input/output 334. Cognitivedata 342 may also be generated automatically by cognitive test engine343. Cognitive test engine 343 is software for administering cognitivetests and psychological tests to a subject. Cognitive test engine 343may use input/output 334 to present a set of cognitive and psychologicaltest questions to the subject. The subject enters answers usinginput/output 334. The set of questions may be presented in an audioformat, a video format, a tactile format, or a combination of audio,video, and/or tactile format. Cognitive test engine 343 analyzes thesubject's responses to set of questions and generates cognitive data 342based on the answers.

Plurality of scans 344 is a plurality of brain scans for a plurality ofsubjects. In other words, plurality of scans 344 includes brain scans ofmultiple different subjects taken at various time periods. Plurality ofscans 344 includes a first set of brain scans for each subject in theplurality of subjects generated at a first time period and a second setof brain scans for each subject in the plurality of subjects generatedat a second time period. In other words, each subject in the pluralityof subjects has a series of scans of the subject's brain generated andstored in plurality of scans 344. The scans may be generated on a weeklybasis, on a biweekly basis, on a monthly basis, every six months, orover any other cyclic period of time. In another embodiment, pluralityof scans 344 includes scans generated at various irregular times. Forexample, the first set of scans may be taken when therapy begins, asecond set of scans may be generated three months later, and a third setof scans may be generated five months later.

In this example, first set of scans 303 and second set of scans 304 arescans for subject A in plurality of scans 344. Plurality of scans 344also includes a first set of scans and a second set of scans for everyother subject in plurality of subjects 344. For example, if theplurality of subjects includes subject B and subject C, then pluralityof scans 344 would include a first set of scans for subject B, a firstset of scans for subject C, a second set of scans for subject B, and asecond set of scans for subject C wherein the first set of scans forsubject B is generated at a different time period than the second set ofscans for subject B. Although FIG. 3 only shows two set of scans forsubject A, any number of sets of scans may be generated. A subject mayhave ten sets of scans taken at different time periods, one-hundred setsof scans taken at different times, or any other number of sets of scanstaken at different times.

The plurality of subjects may include subjects from various demographicgroups. The various demographic groups are groups having differentcharacteristics, such as age, age range, gender, nationality, race,pre-existing conditions, and other characteristics. Each subject in theplurality of subjects is diagnosed with a given condition in common. Forexample, and without limitation, all the subjects in the plurality ofsubjects may be diagnosed with depression. Each subject is receiving agiven therapy. The given therapy may be a pharmacotherapy, a mechanicaltherapy, or talk therapy. In this embodiment, each subject in theplurality of subjects receives the given therapy after the first set ofscans were taken at the first time and before the second set of scanswere taken at the second time. However, in another embodiment, the firstset of scans and the second set of scans may be taken after the giventherapy is received by the subject. For example, and without limitation,a subject may have the first set of scans taken one day or one weekafter therapy begins and the second set of scans may be taken six monthsafter therapy begins.

Medical data and text analytics 314 searches set of electronic medicalliterature sources 316 for portions of medical literature associatedwith the given therapy. The portions of the medical literature mayinclude, without limitation, portions of medical literature describingthe given condition, symptoms of the given condition, the given therapy,pre-existing conditions in a subject, and/or changes in regions ofinterest 332 occurring over time.

As described above, neuroimage analyzer 302 identifies a first set ofregions of interest in the first set of brain scans for each subject inthe plurality of subjects. Neuroimage analyzer 302 also identifies asecond set of regions of interest in the second set of brain scans thatare automatically identified for each subject. A region of interest isan area in a scan that indicates an effect of the given condition and/oran effect of the given therapy. A set of changes in the set of brainscans is identified for each subject based on a comparison of the firstset of regions of interest for each subject with the second set ofregions of interest for each subject. The set of changes includeindicators of change occurring in corresponding regions of interestafter each subject begins receiving the given therapy. An indicator maybe a level of brain activity, a level of brain metabolism, a level of abrain chemical, a characteristic of a brain structure, a size of a brainstructure, or any other characteristic of a feature of a region in abrain scan. For example, and without limitation, an indicator may be asize of a brain ventricle or a level of neurotransmitters in an area ofthe brain.

Therapy mechanism generator 348 is software for determining a drugmechanism of action based on an analysis of changes in the regions ofinterest for the plurality of subjects and the portions of the medicalliterature associated with the given condition and the given therapy.Therapy mechanism generator 348 analyzes the set of changes for eachsubject with the portions of the medical literature describing the giventherapy to identify set of typical changes 354 attributable to the giventherapy. Set of typical changes 354 are changes that occur consistentlyin corresponding regions of interest in a set of subjects. A change isnot required to occur in the corresponding regions of interest in everysubject in the plurality of subjects. The change is only required tooccur in a given number of subjects to be determined to be a change thatoccurs consistently in corresponding regions of interest. The changes inset of typical changes 354 are changes associated with the mechanism ofaction of the given therapy and not changes caused by pre-existingconditions, other therapies received by one or more subjects, or anyother external environmental factors.

Therapy mechanism generator 348 analyzes the portions of the medicalliterature with set of typical changes 354 occurring over time to derivemechanism of action 350 for the given therapy. Therapy mechanismgenerator 348 may also analyze additional subject data for each subjectwith set of typical changes 354 and the portions of the medicalliterature to derive mechanism of action 350. The additional subjectdata may include clinical data 336, subject medical history 338,behavioral data 340, and/or cognitive data 342. Therapy mechanismgenerator 348 may be configured to remove any changes from set oftypical changes 354 that are uncorrelated with the given conditionand/or the given therapy. For example, if therapy mechanism generator348 identifies a given change as being attributable to other therapiesbeing applied to a given subject in the plurality of subjects and/orother medical conditions in the subject, therapy mechanism generator 348removes the given change from set of typical changes 354. Changes in theset of scans for the given subject that are attributable to the othertherapies and/or the other medical conditions are identified asuncorrelated changes. All uncorrelated changes are removed from set oftypical changes 354 by therapy mechanism generator 348.

Therapy mechanism generator 348 generates mechanism of action 350 basedon set of typical changes 354 and quantitative information describingthe given condition, the given therapy, and responses to the giventherapy by subjects found in the portions of the medical literature. Inone embodiment, mechanism of action 350 comprises a set of links to theportions of the medical literature associated with the given therapyand/or a set of links to each change in set of typical changes 350.

In this embodiment, neuroimage mapping manager 300 and therapy mechanismgenerator 348 are located on computer 301. In another embodiment,therapy mechanism generator 348 may be located on a computing devicethat is remote from computer 301, such as, without limitation, a remoteserver. In such a case, therapy mechanism generator 348 may receivechanges in regions of interest 332 and/or the sets of regions ofinterests for each subject via a network connection to a network, suchas network 102 in FIG. 1. The network may be a intranet, Ethernet, theInternet, a local area network (LAN), a wide area network (WAN), awireless network, a private network, or any other type of network.

Referring to FIG. 4, a block diagram of a magnetic resonance imagingbrain scan having regions of interest is depicted in accordance with anillustrative embodiment. Scan 400 is a positron emission tomography scanof a brain of a subject. Scan 402 is a positron emission tomography scanof a normal, healthy subject. Scan 400 has regions of interest 404-408.Regions of interest 404-408 are areas in scan 400 that show indicationsof a potential condition, abnormality, chemical imbalance, illness,disease, or other deviation from an expected appearance of the scan. Inthis example, regions of interest 404-408 show disruptions in brainactivity. Region 406 shows abnormal changes in the size of theventricles of the brain. Region 408 shows decreased function in thefrontal section.

Turning now to FIG. 5, a positron emissions tomography brain scan havingregions of interest is shown in accordance with an illustrativeembodiment. Scan 500 is a magnetic resonance imaging scan of a subject'sbrain. Scan 502 is a magnetic resonance imaging scan of a normal,healthy subject's brain. Scan 500 includes region of interest 504.Region 504 shows an enlargement of the ventricles of the brain whencompared with scan 502 of a normal, healthy subject. The enlargement ofthe ventricles shown in region of interest 504 may indicate an illnessor disease, such as, without limitation, schizophrenia. Therefore, aneuroimage mapping manager identifies region 504 as a region ofinterest.

FIG. 6 is a block diagram of digital video analysis for generatingbehavioral data in accordance with an illustrative embodiment. Digitalvideo analysis 600 is software for generating metadata describing thebehavior of a subject by analyzing video images of the subject, such as,without limitation, International Business Machines (IBM) SmartSurveillance System (S3). Set of cameras 602 is a set of one or morecameras. Set of cameras 602 generates video data and/or audio data 604.

Digital video analysis 600 receives video data and/or audio data 604from set of cameras 602. Video analysis 606 is a video analytics enginethat automatically analyzes video images and generates video metadata610 describing events occurring in the video data. For example, if thevideo data is a continuous video stream having images of subject pacingin a circle, video metadata 610 describes the speed at which the subjectis pacing, the path along which the subject walks as the subject paces,and any other movements made by the subject as the subject paces.

Audio analytics 608 is an analytics engine that analyzes audio datarecorded by a set of microphones and generates audio metadata 612describing the sounds in the audio data. For example, and withoutlimitation, audio metadata 612 may identify words spoken by the subject,the decibel level of sound, the origination point of the sound, thepitch of the sound, the type of sound, or any other description of thesound. The type of sound is an identification of what made a sound. Atype of sound may be a human voice, a human cry, a sound of a footfall,tapping, humming, or any other type of sound. Behavior analysis 614analyzes video metadata 610 and audio metadata 612 using data models 616to identify events. The identified events are described in behavioraldata 622.

FIG. 7 is a flowchart of a process for generating baseline control scansin accordance with an illustrative embodiment. The process in FIG. 7 maybe implemented by software for generating a set of baseline controlscans, such as medical data and text analytics 314 in FIGS. 3A and 3B.The baseline control scans may include baseline normal scans and/orbaseline abnormal scans. Baseline normal scans are scans depictingregions of a brain that does not show indications of at least oneneuropsychiatric disorder. Baseline abnormal scans are scans depictingregions of a brain that does show one or more indications of at leastone neuropsychiatric disorder.

The process begins by generating baseline normal scans based on a set ofscans for average healthy subjects in various demographic groups (step702). The medical data and text analytics may obtain the set of scansfor the healthy subjects by searching a set of medical literaturesources for the scans of normal, healthy subjects. The scans of thenormal, healthy subjects may be saved in a data storage device to formbaseline normal scans.

The medical data and text analytics generates baseline abnormal scansbased on a set of scans for subjects in various demographic groupsdiagnosed with identified conditions (step 704). The medical data andtext analytics may obtain the set of scans for subjects with theidentified conditions by searching the set of medical literature sourcesfor scans of subjects having known and/or diagnosed conditions. Theconditions may be a disease, an illness, an infection, a deformity, orany other condition. The scans of the subjects having the knownconditions may be saved in the data storage device to form baselineabnormal scans (step 708). The medical data and text analytics generatesbaseline treatment scans based on a set of scans for subjects in variousdemographic groups diagnosed with identified conditions and undergoingidentified therapies and/or treatment (step 706) with the processterminating thereafter.

FIG. 8 is a flowchart of a process for identifying regions of interestcorrelated with relevant portions of the medical interest in accordancewith an illustrative embodiment. The process in FIG. 8 may beimplemented by software for analyzing subject brain scans to identifyregions of interest in the brain scans and generate links to portions ofinterest in the medical literature, such as neuroimage mapping manager300 in FIGS. 3A and 3B.

The neuroimage mapping manager receives a set of scans for a subject(step 802). The set of scans may include, without limitation, functionalmagnetic resonance imaging (FMRI) scans, structural magnetic resonanceimaging (sMRI) scans, positron emission tomography (PET) scans, or anyother type of brain scans. The neuroimage mapping manager analyzes theset of scans to identify regions of interest in the scans based onbaseline normal scans and/or baseline abnormal scans for identifieddisorders (step 804). The neuroimage mapping manager displays theidentified regions of interest to a user (step 806). The neuroimagemapping manager makes a determination as to whether a selection of oneor more additional regions of interest is received from the user (step808).

If a selection of one or more additional regions of interest is receivedfrom the user, the neuroimage mapping manager adds the one or moreselected regions to the regions of interest (step 810). After adding theselected regions to the regions of interest at step 810 or if noselections of additional regions are received from the user at step 808,the neuroimage mapping manager retrieves relevant medical literaturefrom a set of sources using search engines, pattern recognition,queries, and/or data mining (step 812). The embodiments are not limitedto using only search engines, queries, and data mining. Any known oravailable method for locating desired information in an electronic datasource may be utilized.

Next, the neuroimage mapping manager identifies portions of interest inthe medical literature associated with and/or describing the regions ofinterest (step 814). The portions of interest may include pages,paragraphs, or portions of text describing one or more of the regions ofinterest, the appearance of one or more of the regions of interest, orthe characteristics of one or more of the regions of interest. Theportions of interest in the relevant medical literature may includeimages of scans containing one or more of the regions of interest,portions of text in the medical literature describing diseases,deficiencies, illnesses, and/or abnormalities that may cause theappearance of one or more of the regions of interest or one or morecharacteristics of the regions of interest, or any other portion ofmedical literature that is relevant to one or more of the regions ofinterest in the subject's scans. The neuroimage mapping manager outputsresults identifying the regions of interest with a set of links to theportions of interest in the medical literature (step 816) with theprocess terminating thereafter.

In this embodiment, the regions of interest are displayed to the userand the user is given an opportunity to select one or more additionalregions of interest to add to the regions of interest identified by theneuroimage mapping manager. In another embodiment, the regions ofinterest are not presented to the user prior to identifying the portionsof interest in the medical literature. In this embodiment, the user isnot required to review the regions of interest and provide input as towhether to add one or more additional regions of interest. In this case,the process may occur completely automatically without any user inputduring the process of analyzing the subject's scans to identify regionsof interest and linking portions of the relevant medical literature tothe regions of interest.

FIG. 9 is a flowchart of a process for generating potential diagnosesfor a subject based on quantitative information derived from a set ofbrain scans in accordance with an illustrative embodiment. The processin FIG. 9 is implemented by software for automatically generating amechanism of action for a given therapy based on neuroimage data andportions of the medical literature, such as, without limitation,mechanism of action generator 348 in FIGS. 3A and 3B.

The process begins by receiving identified regions of interest andcorrelated portions of medical literature (step 902). A determination ismade as to whether a medical history for the subject is available (step904). If a medical history is available, the medical history isretrieved (step 906). After retrieving the medical history at step 906,or if the medical history is not available at step 906, a determinationis made as to whether clinical data is available (step 908). If clinicaldata is available, the clinical data is retrieved (step 910). Afterretrieving the clinical data at step 910 or if clinical data is notavailable at step 908, a determination is made as to whether behavioraldata is available (step 912). If behavioral data is available, thebehavioral data is retrieved (step 914). After retrieving the behavioraldata at step 914 or if behavioral data is not available, the diagnosticengine analyzes the regions of interest and portions of medicalliterature with any available clinical data, medical history, and/orbehavioral data to identify correlated and uncorrelated changes in a setof changes occurring in a subject over time (step 916). Correlatedchanges are changes that are correlated with the given therapy.Uncorrelated changes are changes that are not attributable to the giventherapy. Uncorrelated changes may be due to other therapies,pre-existing conditions, or other causes unrelated to the given therapy.Uncorrelated changes are removed from a set of typical changes for aparticular mechanism of action (step 918) with the process terminatingthereafter.

The steps shown in the flowcharts may be executed in a different orderthan the order shown in FIG. 9. For example, clinical data may beretrieved prior to retrieving the medical history or simultaneously withretrieving the medical history. Likewise, the behavioral data may beretrieved prior to retrieving either clinical data or medical historydata. Likewise, some of the steps in FIG. 9 may be optional. Forexample, and without limitation, the process does not require retrievalof clinical data, retrieval of medical history data, or retrieval ofbehavioral data. Thus, the diagnostic engine may generate a set ofpotential diagnoses in step 918 without requiring an analysis ofclinical data, medical history, and/or behavioral data.

FIG. 10 is a flowchart of a process for generating a mechanism of actionfor a given therapy in accordance with an illustrative embodiment. Theprocess in FIG. 10 may be implemented by software for analyzing changesin neuroimage data occurring over time and relevant portions ofelectronic medical literature to generate mechanisms of action fortherapies, such as mechanism of action generator 348 in FIG. 3B. Steps1004-1006 may be implemented by software for identifying regions ofinterest and changes in regions of interest over time, such as medicaldata and text analytics 314 in FIG. 3A.

The process begins by receiving a plurality of brain scans for aplurality of subjects having a given condition and receiving aparticular therapy (step 1002). The process identifies regions ofinterest in the brain scans in the plurality of brain scans (step 1004).The process identifies changes in the regions of interest in the brainscans occurring over time (step 1006). The process compares the changesin the regions of interests occurring over time for each subject toidentify a set of typical changes attributable to the given therapy(step 1008). The process analyzes portions of the medical literatureassociated with the given therapy, the given condition, and the set oftypical changes (step 1010). The process generates the mechanism ofaction for the given therapy based on the analysis of the set of typicalchanges in the regions of interest and the portions of the medicalliterature (step 1012) with the process terminating thereafter.

Thus, in one embodiment, a computer implemented method, apparatus, andcomputer program product of determining mechanisms of action fortherapies is provided. The process receives a plurality of brain scansfor a plurality of subjects. The plurality of brains scans comprises afirst set of brain scans for each subject in the plurality of subjectsgenerated at a first time period and a second set of brain scans foreach subject in the plurality of subjects generated at a second timeperiod. Each subject in the plurality of subjects is diagnosed with agiven condition and each subject is receiving a given therapy. The giventherapy may be a pharmacotherapy, a mechanical therapy, or talk therapy.

A set of electronic medical literature sources is automatically searchedfor portions of medical literature describing the given therapy. A firstset of regions of interest in the first set of brain scans for eachsubject and a second set of regions of interest in the second set ofbrain scans are automatically identified for each subject. A set ofchanges in the set of brain scans is identified for each subject basedon a comparison of the first set of regions of interest for each subjectwith the second set of regions of interest for each subject. The set ofchanges is change occurring after each subject begins receiving thegiven therapy. The set of changes for each subject is analyzed with theportions of the medical literature describing the given therapy toidentify a set of typical changes attributable to the given therapy. Amechanism of action for the given therapy is generated based on the setof typical changes.

The therapy mechanism generator automatically determines a mechanism ofaction of a given therapy, such as, without limitation, a drug therapy,talk therapy, or mechanical therapy, without requiring human input andhuman analysis of the medical literature, brain scans, and otheradditional subject information. The therapy mechanism generatorgenerates mechanism of action faster and more efficiently that in theprior art. In addition, the utilization of electronic sources ofrelevant portions of medical literature and analysis of neuroimage dataenables more accurate determination of mechanisms of action in complexcases.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The invention can take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In a preferred embodiment, the invention isimplemented in software, which includes but is not limited to firmware,resident software, microcode, etc.

Furthermore, the invention can take the form of a computer programproduct accessible from a computer-usable or computer-readable mediumproviding program code for use by or in connection with a computer orany instruction execution system. For the purposes of this description,a computer-usable or computer readable medium can be any tangibleapparatus that can contain, store, communicate, or transport the programfor use by or in connection with the instruction execution system,apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device). Examples ofa computer-readable medium include a semiconductor or solid statememory, magnetic tape, a removable computer diskette, a random accessmemory (RAM), a read-only memory (ROM), a rigid magnetic disk and anoptical disk. Current examples of optical disks include compactdisk—read only memory (CD-ROM), compact disk—read/write (CD-R/W) andDVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A computer implemented method of assessingneuroimaging and medical data to determine mechanisms of action forneuropsychiatric therapies, the computer implemented method comprising:receiving, at a processor from a first non-transitory computer readablestorage medium via a network connection, neuroimaging data of a firstset of human brain scans for each human subject in a plurality of humansubjects diagnosed with a given neuropsychiatric condition generated ata first time period prior to beginning implementation of aneuropsychiatric therapy to treat the given neuropsychiatric conditionand a second set of human brain scans for the each human subject in theplurality of human subjects diagnosed with the given neuropsychiatriccondition generated at a second time period of a given amount of timeafter beginning the implementation of the neuropsychiatric therapy totreat the given neuropsychiatric condition, wherein a set of one or morescanning devices generate the neuroimaging data of the first set ofhuman brain scans and the second set of human brain scans, and whereinthe neuropsychiatric therapy is received by the each human subject inthe plurality of human subjects diagnosed with the givenneuropsychiatric condition; automatically searching, using theprocessor, via the network connection a set of online electronic medicalliterature sources stored in a second non-transitory computer readablestorage medium for portions of medical literature describing theneuropsychiatric therapy used to treat the given neuropsychiatriccondition, wherein the set of online electronic medical literaturesources comprises at least one of published journal articles, medicalstudy results, medical texts, or pharmaceutical studies; automaticallyidentifying, using the processor, a first set of regions of interest inthe first set of human brain scans for the each human subject in theplurality of human subjects diagnosed with the given neuropsychiatriccondition generated at the first time period prior to beginning theimplementation of the neuropsychiatric therapy to treat the givenneuropsychiatric condition and a second set of regions of interest inthe second set of human brain scans for the each human subject in theplurality of human subjects diagnosed with the given neuropsychiatriccondition generated at the second time period of the given amount oftime after beginning the implementation of the neuropsychiatric therapyto treat the given neuropsychiatric condition; identifying, using theprocessor, a set of changes in the neuroimaging data of the first set ofhuman brain scans and the second set of human brain scans for the eachhuman subject in the plurality of human subjects diagnosed with thegiven neuropsychiatric condition based on a comparison of the first setof regions of interest for the each human subject with the second set ofregions of interest for the each human subject, wherein the set ofchanges comprises indicators of change occurring after the each humansubject began receiving the neuropsychiatric therapy to treat the givenneuropsychiatric condition; analyzing, using the processor, the set ofchanges for the each human subject with the portions of the medicalliterature describing the neuropsychiatric therapy used to treat thegiven neuropsychiatric condition to identify a set of changesattributable to the neuropsychiatric therapy; identifying, using theprocessor, other therapies unrelated to treatment of the givenneuropsychiatric condition being applied to one or more human subjectsin the plurality of human subjects diagnosed with the givenneuropsychiatric condition; identifying, using the processor, changes inthe neuroimaging data of the first set of human brain scans and thesecond set of human brain scans for the one or more human subjects inthe plurality of human subjects diagnosed with the givenneuropsychiatric condition that are attributable to the other therapiesunrelated to the treatment of the given neuropsychiatric condition beingapplied to the one or more human subjects to form uncorrelated changesattributable to the other therapies unrelated to the treatment of thegiven neuropsychiatric condition; eliminating, using the processor, theuncorrelated changes attributable to the other therapies unrelated tothe treatment of the given neuropsychiatric condition from the set ofchanges identified in the first set of human brain scans and the secondset of human brain scans for the each human subject in the plurality ofhuman subjects diagnosed with the given neuropsychiatric condition;generating, using the processor, a mechanism of action for theneuropsychiatric therapy for the plurality of human subjects diagnosedwith the given neuropsychiatric condition based on the analyzing of theset of changes for the each human subject, wherein the mechanism ofaction comprises a set of links to the portions of the medicalliterature associated with the neuropsychiatric therapy and each changein the set of changes identified from an automatic searching of the setof online electronic medical literature sources; generating, using theprocessor, the set of links to the portions of the medical literatureassociated with the neuropsychiatric therapy used to treat the givenneuropsychiatric condition; embedding, using the processor, the set oflinks to the portions of the medical literature associated with theneuropsychiatric therapy used to treat the given neuropsychiatriccondition within the first set of regions of interest located in thefirst set of human brain scans for the each human subject in theplurality of human subjects diagnosed with the given neuropsychiatriccondition and the second set of regions of interest located in thesecond set of human brain scans for the each human subject in theplurality of human subjects diagnosed with the given neuropsychiatriccondition; and storing the mechanism of action for the neuropsychiatrictherapy in a third non-transitory computer readable storage medium viathe network connection.
 2. The computer implemented method of claim 1further comprising: analyzing additional subject data for the each humansubject in the plurality of human subjects diagnosed with the givenneuropsychiatric condition with the set of changes identified in thefirst set of human brain scans and the second set of human brain scansfor the each human subject and the portions of the medical literatureassociated with the neuropsychiatric therapy to generate the mechanismof action for the neuropsychiatric therapy, wherein the mechanism ofaction comprises the set of changes identified in the first set of humanbrain scans and the second set of human brain scans of the plurality ofhuman subjects and changes in clinical test results occurring over time,and wherein the additional subject data comprises at least one ofclinical data, subject medical history, behavioral data, and cognitivedata.
 3. The computer implemented method of claim 1 further comprising:searching the set of online electronic medical literature sources formedical literature relevant to the given neuropsychiatric condition, theneuropsychiatric therapy, and the set of changes identified in the firstset of human brain scans and the second set of human brain scans for theeach human subject in the plurality of human subjects diagnosed with thegiven neuropsychiatric condition occurring over time to form theportions of the medical literature; and analyzing the portions of themedical literature with the set of changes identified in the first setof human brain scans and the second set of human brain scans for theeach human subject in the plurality of human subjects diagnosed with thegiven neuropsychiatric condition occurring over time to derive themechanism of action for the neuropsychiatric therapy.
 4. The computerimplemented method of claim 1 wherein the plurality of human subjectscomprises human subjects from various demographic groups, and whereinthe each human subject in the plurality of human subjects diagnosed withthe given neuropsychiatric condition receives the neuropsychiatrictherapy after the first set of human brain scans were taken at the firsttime and before the second set of human brain scans were taken at thesecond time.
 5. The computer implemented method of claim 1 furthercomprising: receiving a set of human brain scans for a set of healthyhuman subjects in various demographic groups to form baseline normalhuman brain scans; analyzing the baseline normal human brain scans toidentify a normal appearance of areas in normal human brain scans,wherein a normal human brain scan is a scan that does not showindications of disease or abnormalities in the areas in the normal humanbrain scans; and comparing the set of changes identified in the firstset of human brain scans and the second set of human brain scans for theeach human subject in the plurality of human subjects diagnosed with thegiven neuropsychiatric condition with the baseline normal human brainscans to identify the mechanism of action for the neuropsychiatrictherapy.
 6. The computer implemented method of claim 1 wherein the firstset of human brain scans and the second set of human brain scans bothcomprise a combination of one or more positron emission tomography scansand one or more magnetic resonance imaging scans of a human subject inthe plurality of human subjects diagnosed with the givenneuropsychiatric condition.
 7. The computer implemented method of claim1 further comprising: identifying other medical conditions associatedwith one or more human subjects in the plurality of human subjectsdiagnosed with the given neuropsychiatric condition; identifying changesin the first set of human brain scans and the second set of human brainscans for the one or more human subjects that are attributable to theother medical conditions associated with the one or more human subjectsto form uncorrelated changes attributable to the other medicalconditions associated with the one or more human subjects; and removingthe uncorrelated changes attributable to the other medical conditionsassociated with the one or more human subjects from the set of changesidentified in the first set of human brain scans and the second set ofhuman brain scans for the each human subject in the plurality of humansubjects diagnosed with the given neuropsychiatric condition.
 8. Thecomputer implemented method of claim 1 wherein the neuropsychiatrictherapy used to treat the given neuropsychiatric condition is apharmacotherapy, and wherein the mechanism of action for theneuropsychiatric therapy is a drug mechanism of action.
 9. The computerimplemented method of claim 1 wherein the neuropsychiatric therapy usedto treat the given neuropsychiatric condition is selected from a groupconsisting of a mechanical therapy and a talk therapy.
 10. Anon-transitory computer readable storage medium having computer usableprogram code for assessing neuroimaging and medical data to determinemechanisms of action for neuropsychiatric therapies embodied therewiththat is executable by a computer, the computer usable program codecomprising: computer usable program code configured to receive via anetwork connection neuroimaging data of a first set of human brain scansfor each human subject in a plurality of human subjects diagnosed with agiven neuropsychiatric condition generated at a first time period priorto beginning implementation of a neuropsychiatric therapy to treat thegiven neuropsychiatric condition and a second set of human brain scansfor the each human subject in the plurality of human subjects diagnosedwith the given neuropsychiatric condition generated at a second timeperiod of a given amount of time after beginning the implementation ofthe neuropsychiatric therapy to treat the given neuropsychiatriccondition, wherein a set of one or more scanning devices generate theneuroimaging data of the first set of human brain scans and the secondset of human brain scans, and wherein the neuropsychiatric therapy isreceived by the each human subject in the plurality of human subjectsdiagnosed with the given neuropsychiatric condition; computer usableprogram code configured to automatically search via the networkconnection a set of online electronic medical literature sources storedin a second non-transitory computer readable storage medium for portionsof medical literature describing the neuropsychiatric therapy used totreat the given neuropsychiatric condition, wherein the set of onlineelectronic medical literature sources comprises at least one ofpublished journal articles, medical study results, medical texts, orpharmaceutical studies; computer usable program code configured toautomatically select a first set of regions of interest in the first setof human brain scans for the each human subject in the plurality ofhuman subjects diagnosed with the given neuropsychiatric conditiongenerated at the first time period prior to beginning the implementationof the neuropsychiatric therapy to treat the given neuropsychiatriccondition and a second set of regions of interest in the second set ofhuman brain scans for the each human subject in the plurality of humansubjects diagnosed with the given neuropsychiatric condition generatedat the second time period of the given amount of time after beginningthe implementation of the neuropsychiatric therapy to treat the givenneuropsychiatric condition; computer usable program code configured toidentify a set of changes in the neuroimaging data of the first set ofhuman brain scans and the second set of human brain scans for the eachhuman subject in the plurality of human subjects diagnosed with thegiven neuropsychiatric condition based on a comparison of the first setof regions of interest for the each human subject with the second set ofregions of interest for the each human subject, wherein the set ofchanges comprises indicators of change occurring after the each humansubject began receiving the neuropsychiatric therapy to treat the givenneuropsychiatric condition; computer usable program code configured toanalyze the set of changes for the each human subject with the portionsof the medical literature describing the neuropsychiatric therapy usedto treat the given neuropsychiatric condition to identify a set ofchanges attributable to the neuropsychiatric therapy; computer usableprogram code configured to identify other therapies unrelated totreatment of the given neuropsychiatric condition being applied to oneor more human subjects in the plurality of human subjects diagnosed withthe given neuropsychiatric condition; computer usable program codeconfigured to identify changes in the neuroimaging data of the first setof human brain scans and the second set of human brain scans for the oneor more human subjects in the plurality of human subjects diagnosed withthe given neuropsychiatric condition that are attributable to the othertherapies unrelated to the treatment of the given neuropsychiatriccondition being applied to the one or more human subjects to formuncorrelated changes attributable to the other therapies unrelated tothe treatment of the given neuropsychiatric condition; computer usableprogram code configured to eliminate the uncorrelated changesattributable to the other therapies unrelated to the treatment of thegiven neuropsychiatric condition from the set of changes identified inthe first set of human brain scans and the second set of human brainscans for the each human subject in the plurality of human subjectsdiagnosed with the given neuropsychiatric condition; computer usableprogram code configured to generate a mechanism of action for theneuropsychiatric therapy for the plurality of human subjects diagnosedwith the given neuropsychiatric condition based on the analyzing of theset of changes for the each human subject, wherein the mechanism ofaction comprises a set of links to the portions of the medicalliterature associated with the neuropsychiatric therapy and each changein the set of changes identified from an automatic searching of the setof online electronic medical literature sources; computer usable programcode configured to generate the set of links to the portions of themedical literature associated with the neuropsychiatric therapy used totreat the given neuropsychiatric condition; and computer usable programcode configured to embed the set of links to the portions of the medicalliterature associated with the neuropsychiatric therapy used to treatthe given neuropsychiatric condition within the first set of regions ofinterest located in the first set of human brain scans for the eachhuman subject in the plurality of human subjects diagnosed with thegiven neuropsychiatric condition and the second set of regions ofinterest located in the second set of human brain scans for the eachhuman subject in the plurality of human subjects diagnosed with thegiven neuropsychiatric condition.
 11. The non-transitory computerreadable storage medium of claim 10 wherein the computer usable programcode further comprises: computer usable program code configured toanalyze additional subject data for the each human subject in theplurality of human subjects diagnosed with the given neuropsychiatriccondition with the set of changes identified in the first set of humanbrain scans and the second set of human brain scans for the each humansubject and the portions of the medical literature associated with theneuropsychiatric therapy to generate the mechanism of action for theneuropsychiatric therapy, wherein the mechanism of action comprises theset of changes identified in the first set of human brain scans and thesecond set of human brain scans of the plurality of human subjects andchanges in clinical test results occurring over time, and wherein theadditional subject data comprises at least one of clinical data, subjectmedical history, behavioral data, and cognitive data.
 12. Thenon-transitory computer readable storage medium of claim 10 wherein thecomputer usable program code further comprises: computer usable programcode configured to search the set of online electronic medicalliterature sources for medical literature relevant to the givenneuropsychiatric condition, the neuropsychiatric therapy, and the set ofchanges identified in the first set of human brain scans and the secondset of human brain scans for the each human subject in the plurality ofhuman subjects diagnosed with the given neuropsychiatric conditionoccurring over time to form the portions of the medical literature; andcomputer usable program code configured to analyze the portions of themedical literature with the set of changes identified in the first setof human brain scans and the second set of human brain scans for theeach human subject in the plurality of human subjects diagnosed with thegiven neuropsychiatric condition occurring over time to derive themechanism of action for the neuropsychiatric therapy.
 13. Thenon-transitory computer readable storage medium of claim 10 wherein theplurality of human subjects comprises human subjects from variousdemographic groups, and wherein the each human subject in the pluralityof human subjects diagnosed with the given neuropsychiatric conditionreceives the neuropsychiatric therapy after the first set of human brainscans were taken at the first time and before the second set of humanbrain scans were taken at the second time.
 14. The non-transitorycomputer readable storage medium of claim 10 wherein the computer usableprogram code further comprises: computer usable program code configuredto receive a set of human brain scans for a set of healthy humansubjects in various demographic groups to form baseline normal humanbrain scans; computer usable program code configured to analyze thebaseline normal human brain scans to identify a normal appearance ofareas in normal human brain scans, wherein a normal human brain scan isa scan that does not show indications of disease or abnormalities in theareas in the normal human brain scans; and computer usable program codeconfigured to compare the set of changes identified in the first set ofhuman brain scans and the second set of human brain scans for the eachhuman subject in the plurality of human subjects diagnosed with thegiven neuropsychiatric condition with the baseline normal human brainscans to identify the mechanism of action for the neuropsychiatrictherapy.
 15. The non-transitory computer readable storage medium ofclaim 10 wherein the computer usable program code further comprises:computer usable program code configured to identify other medicalconditions associated with one or more human subjects in the pluralityof human subjects diagnosed with the given neuropsychiatric condition;computer usable program code configured to identify changes in the firstset of human brain scans and the second set of human brain scans for theone or more human subjects that are attributable to the other medicalconditions associated with the one or more human subjects to formuncorrelated changes attributable to the other medical conditionsassociated with the one or more human subjects; and computer usableprogram code configured to remove the uncorrelated changes attributableto the other medical conditions associated with the one or more humansubjects from the set of changes identified in the first set of humanbrain scans and the second set of human brain scans for the each humansubject in the plurality of human subjects diagnosed with the givenneuropsychiatric condition.
 16. The non-transitory computer readablestorage medium of claim 10 wherein the neuropsychiatric therapy used totreat the given neuropsychiatric condition is selected from a groupconsisting of a pharmacotherapy, a mechanical therapy, and a talktherapy.
 17. An apparatus for assessing neuroimaging and medical data todetermine mechanisms of action for neuropsychiatric therapiescomprising: a bus system; a communications system coupled to the bussystem; a non-transitory computer readable storage medium connected tothe bus system, wherein the non-transitory computer readable storagemedium stores computer usable program code; and a processor chip coupledto the bus system, wherein the processor chip executes the computerusable program code to: receive via a network connection neuroimagingdata of a first set of human brain scans for each human subject in aplurality of human subjects diagnosed with a given neuropsychiatriccondition generated at a first time period prior to beginningimplementation of a neuropsychiatric therapy to treat the givenneuropsychiatric condition and a second set of human brain scans for theeach human subject in the plurality of human subjects diagnosed with thegiven neuropsychiatric condition generated at a second time period of agiven amount of time after beginning the implementation of theneuropsychiatric therapy to treat the given neuropsychiatric condition,wherein a set of one or more scanning devices generate the neuroimagingdata of the first set of human brain scans and the second set of humanbrain scans, and wherein the neuropsychiatric therapy is received by theeach human subject in the plurality of human subjects diagnosed with thegiven neuropsychiatric condition; automatically search via the networkconnection a set of online electronic medical literature sources storedin a second non-transitory computer readable storage medium for portionsof medical literature describing the neuropsychiatric therapy used totreat the given neuropsychiatric condition, wherein the set of onlineelectronic medical literature sources comprises at least one ofpublished journal articles, medical study results, medical texts, orpharmaceutical studies; analyze the first set of brain scans and thesecond set of brain scans to identify a first set of regions of interestin the first set of human brain scans for the each human subject in theplurality of human subjects diagnosed with the given neuropsychiatriccondition generated at the first time period prior to beginning theimplementation of the neuropsychiatric therapy to treat the givenneuropsychiatric condition and a second set of regions of interest inthe second set of human brain scans for the each human subject in theplurality of human subjects diagnosed with the given neuropsychiatriccondition generated at the second time period of the given amount oftime after beginning the implementation of the neuropsychiatric therapyto treat the given neuropsychiatric condition; identify a set of changesin the neuroimaging data of the first set of human brain scans and thesecond set of human brain scans for the each human subject in theplurality of human subjects diagnosed with the given neuropsychiatriccondition based on a comparison of the first set of regions of interestfor the each human subject with the second set of regions of interestfor the each human subject, wherein the set of changes comprisesindicators of change occurring after the each human subject beganreceiving the neuropsychiatric therapy to treat the givenneuropsychiatric condition; analyze the set of changes for the eachhuman subject with the portions of the medical literature describing theneuropsychiatric therapy used to treat the given neuropsychiatriccondition to identify a set of changes attributable to theneuropsychiatric therapy; identify other therapies unrelated totreatment of the given neuropsychiatric condition being applied to oneor more human subjects in the plurality of human subjects diagnosed withthe given neuropsychiatric condition; identify changes in theneuroimaging data of the first set of human brain scans and the secondset of human brain scans for the one or more human subjects in theplurality of human subjects diagnosed with the given neuropsychiatriccondition that are attributable to the other therapies unrelated to thetreatment of the given neuropsychiatric condition being applied to theone or more human subjects to form uncorrelated changes attributable tothe other therapies unrelated to the treatment of the givenneuropsychiatric condition; eliminate the uncorrelated changesattributable to the other therapies unrelated to the treatment of thegiven neuropsychiatric condition from the set of changes identified inthe first set of human brain scans and the second set of human brainscans for the each human subject in the plurality of human subjectsdiagnosed with the given neuropsychiatric condition; generate amechanism of action for the neuropsychiatric therapy for the pluralityof human subjects diagnosed with the given neuropsychiatric conditionbased on the analyzing of the set of changes for the each human subject,wherein the mechanism of action comprises a set of links to the portionsof the medical literature associated with the neuropsychiatric therapyand each change in the set of changes identified from an automaticsearching of the set of online electronic medical literature sources;generate the set of links to the portions of the medical literatureassociated with the neuropsychiatric therapy used to treat the givenneuropsychiatric condition; and embed the set of links to the portionsof the medical literature associated with the neuropsychiatric therapyused to treat the given neuropsychiatric condition within the first setof regions of interest located in the first set of human brain scans forthe each human subject in the plurality of human subjects diagnosed withthe given neuropsychiatric condition and the second set of regions ofinterest located in the second set of human brain scans for the eachhuman subject in the plurality of human subjects diagnosed with thegiven neuropsychiatric condition.
 18. The apparatus of claim 17 whereinthe processor chip further executes the computer usable program code toanalyze additional subject data for the each human subject in theplurality of human subjects diagnosed with the given neuropsychiatriccondition with the set of changes identified in the first set of humanbrain scans and the second set of human brain scans for the each humansubject and the portions of the medical literature associated with theneuropsychiatric therapy to generate the mechanism of action for theneuropsychiatric therapy, wherein the mechanism of action comprises theset of changes identified in the first set of human brain scans and thesecond set of human brain scans of the plurality of human subjects andchanges in clinical test results occurring over time, and wherein theadditional subject data comprises at least one of clinical data, subjectmedical history, behavioral data, and cognitive data.
 19. The apparatusof claim 17 wherein the processor chip further executes the computerusable program code to identify other medical conditions associated withone or more human subjects in the plurality of human subjects diagnosedwith the given neuropsychiatric condition; identify changes in the firstset of human brain scans and the second set of human brain scans for theone or more human subjects that are attributable to the other medicalconditions associated with the one or more human subjects to formuncorrelated changes attributable to the other medical conditionsassociated with the one or more human subjects; and remove theuncorrelated changes attributable to the other medical conditionsassociated with the one or more human subjects from the set of changesidentified in the first set of human brain scans and the second set ofhuman brain scans for the each human subject in the plurality of humansubjects diagnosed with the given neuropsychiatric condition.
 20. A dataprocessing system for assessing neuroimaging and medical data todetermine mechanisms of action for neuropsychiatric therapiescomprising: a set of online electronic medical literature sources,wherein the set of online medical literature sources comprises medicalliterature; a neuroimage mapping manager, wherein the neuroimage mappingmanager automatically retrieves via a network connection portions of themedical literature that describe a neuropsychiatric therapy from the setof online electronic medical literature sources, wherein the set ofonline electronic medical literature sources comprises at least one ofpublished journal articles, medical study results, medical texts, orpharmaceutical studies; analyzes neuroimaging data corresponding to aplurality of human brain scans for a plurality of human subjectsdiagnosed with a given neuropsychiatric condition who receive theneuropsychiatric therapy to identify a first set of regions of interestin a first set of human brain scans for each human subject in theplurality of human subjects diagnosed with the given neuropsychiatriccondition and a second set of regions of interest in a second set ofhuman brain scans for each human subject in the plurality of humansubjects diagnosed with the given neuropsychiatric condition andidentifies a set of changes in the neuroimaging data of the first set ofhuman brain scans and the second set of human brain scans for each humansubject in the plurality of human subjects diagnosed with the givenneuropsychiatric condition based on a comparison of the first set ofregions of interest for each human subject with the second set ofregions of interest for each human subject, wherein the first set ofbrain scans for each human subject in the plurality of human subjectsdiagnosed with the given neuropsychiatric condition is generated at afirst time period prior to beginning implementation of theneuropsychiatric therapy to treat the given neuropsychiatric conditionand the second set of human brain scans for each human subject in theplurality of human subjects diagnosed with the given neuropsychiatriccondition is generated at a second time period of a given amount of timeafter beginning the implementation of the neuropsychiatric therapy totreat the given neuropsychiatric condition, and wherein a set of one ormore scanning devices generate the neuroimaging data of the first set ofhuman brain scans and the second set of human brain scans, and whereinthe neuropsychiatric therapy is administered to each human subject inthe plurality of human subjects diagnosed with the givenneuropsychiatric condition, and wherein the set of changes identified inthe first set of human brain scans and the second set of human brainscans for each human subject in the plurality of human subjectsdiagnosed with the given neuropsychiatric condition is changes thatoccur after each human subject began to receive the neuropsychiatrictherapy; and a therapy mechanism generator, wherein the therapymechanism generator analyzes the set of changes for each human subjectwith the portions of the medical literature that describe theneuropsychiatric therapy used to treat the given neuropsychiatriccondition to identify a set of changes attributable to theneuropsychiatric therapy; identifies other therapies unrelated totreatment of the given neuropsychiatric condition being applied to oneor more human subjects in the plurality of human subjects diagnosed withthe given neuropsychiatric condition; identifies changes in theneuroimaging data of the first set of human brain scans and the secondset of human brain scans for the one or more human subjects in theplurality of human subjects diagnosed with the given neuropsychiatriccondition that are attributable to the other therapies unrelated to thetreatment of the given neuropsychiatric condition being applied to theone or more human subjects to form uncorrelated changes attributable tothe other therapies unrelated to the treatment of the givenneuropsychiatric condition; eliminates the uncorrelated changesattributable to the other therapies unrelated to the treatment of thegiven neuropsychiatric condition from the set of changes identified inthe first set of human brain scans and the second set of human brainscans for the each human subject in the plurality of human subjectsdiagnosed with the given neuropsychiatric condition; generates amechanism of action for the neuropsychiatric therapy based on the set ofchanges, wherein the mechanism of action comprises a set of links to theportions of the medical literature associated with the neuropsychiatrictherapy and each change in the set of changes identified from anautomatic searching of the set of online electronic medical literaturesources; generates the set of links to the portions of the medicalliterature associated with the neuropsychiatric therapy used to treatthe given neuropsychiatric condition; and embeds the set of links to theportions of the medical literature associated with the neuropsychiatrictherapy used to treat the given neuropsychiatric condition within thefirst set of regions of interest located in the first set of human brainscans for the each human subject in the plurality of human subjectsdiagnosed with the given neuropsychiatric condition and the second setof regions of interest located in the second set of human brain scansfor the each human subject in the plurality of human subjects diagnosedwith the given neuropsychiatric condition.
 21. The data processingsystem of claim 20 further comprising: a data storage device, whereinthe data storage device stores additional subject data for each humansubject in the plurality of human subjects diagnosed with the givenneuropsychiatric condition, and wherein the therapy mechanism generatoranalyzes the additional subject data for each human subject with the setof changes and portions of the medical literature associated with theneuropsychiatric therapy to generate the mechanism of action for theneuropsychiatric therapy, and wherein the mechanism of action for theneuropsychiatric therapy comprises the set of changes identified in thefirst set of human brain scans and the second set of human brain scansof the plurality of human subjects diagnosed with the givenneuropsychiatric condition and changes in clinical test resultsoccurring over time, and wherein the additional subject data comprisesat least one of clinical data, subject medical history, behavioral data,and cognitive data.
 22. The data processing system of claim 20 whereinthe neuropsychiatric therapy used to treat the given neuropsychiatriccondition is a pharmacotherapy, and wherein the mechanism of action forthe neuropsychiatric therapy is a drug mechanism of action.